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Diffusion methods for generating transition paths
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AI
DOI：10.1016/j.jcp.2024.113590.png)
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Abstract
En 中文
In this work, we seek to simulate rare transitions between metastable states using score-based generative models. An efficient method for generating high-quality transition paths is valuable for the study of molecular systems since data is often difficult to obtain. We develop two novel methods for path generation in this paper: a chain-based approach and a midpoint-based approach. The first biases the original dynamics to facilitate transitions, while the second mirrors splitting techniques and breaks down the original transition into smaller transitions. Numerical results of generated transition paths for the M & uuml;ller potential and for Alanine dipeptide demonstrate the effectiveness of these approaches in both the data-rich and data-scarce regimes.
Keywords:
Diffusion model
Score matching
Transition path
Molecular dynamics
Rare event simulation
Stochastic differential equation
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